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Exact and sampling methods for mining higher-order motifs in large hypergraphs
Title / Series / Name
Computing
Publication Volume
Publication Issue
Pages
Editors
Keywords
05C65
68R10
68W25
Complex networks
Higher-order networks
Hypergraph algorithms
Network motifs
68R10
68W25
Complex networks
Higher-order networks
Hypergraph algorithms
Network motifs
URI
https://hdl.handle.net/20.500.14018/14225
Abstract
Network motifs are recurrent, small-scale patterns of interactions observed frequently in a system. They shed light on the interplay between the topology and the dynamics of complex networks across various domains. In this work, we focus on the problem of counting occurrences of small sub-hypergraph patterns in very large hypergraphs, where higher-order interactions connect arbitrary numbers of system units. We show how directly exploiting higher-order structures speeds up the counting process compared to traditional data mining techniques for exact motif discovery. Moreover, with hyperedge sampling, performance is further improved at the cost of small errors in the estimation of motif frequency. We evaluate our method on several real-world datasets describing face-to-face interactions, co-authorship and human communication. We show that our approximated algorithm allows us to extract higher-order motifs faster and on a larger scale, beyond the computational limits of an exact approach.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2023
Language
ISBN
Identifiers
10.1007/s00607-023-01230-5